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Journal of Environmental Engineering and Landscape Management
ISSN 1648–6897 / eISSN 1822-4199
2021 Volume 29 Issue 3: 305317
https://doi.org/10.3846/jeelm.2021.15567
the cost, and the possibility of analysis of the elements of
space from the notably earlier date (Lillesand & Kiefer,
2000; Tobak etal., 2008). Photo interpretation is one of the
fundamental tools for environment and landscape ecology
(Van Eetvelde & Antrop, 2004; Pinto etal., 2019).
is kind of research is present for a long time and is
actual nowadays too, e.g. as in given examples (Morgan
etal., 2010; Szatmári etal., 2016) in the up to date analysis
of the environment, e.g. aerial photogrammetrical remote
sensing in present (Newton etal., 2009; Van Eetvelde &
USING HISTORICAL AERIAL PHOTOGRAPHY FOR MONITORING OF
ENVIRONMENT CHANGES: A CASE STUDY OF BOVAN LAKE,
EASTERN SERBIA
Saša BAKRAČ1, 6*, Viktor MARKOVIĆ2, Siniša DROBNJAK3, 6, Dejan ĐORĐEVIĆ4,
Nikola STAMENKOVIĆ5
1, 4Military Geographical Institute, Mije Kovačevića 5, 11000 Belgrade, Serbia
2Department for Survey in Military Geographical Institute, Mije Kovačevića 5, 11000 Belgrade, Serbia
3Printing Department in Military Geographical Institute, Mije Kovačevića 5, 11000 Belgrade, Serbia
5Department in Ministry of Defence, Nemanjina 15, 11000 Belgrade, Serbia
6Military Academy, University of Defence, Generala Pavla Jurišića Šturma 33, 11000 Belgrade, Serbia
Received 22 May 2020; accepted 21 May 2021
Highlights
X By analyzing historical aerial photography we can get useful and important information for the spatial, ecological and
many other changes in the environment.
X With a comparative analysis of aerial images from the past and contemporary aerial images, we can get information
about the nature and trends of the observed phenomena.
X is paper gives the obvious demonstration of the possibilities of the usage and the quality of the historical aerial images
for the spatial analysis and the other researches that include the living environment in total.
Abstract. Useful and important information for the spatial, ecological, and many other changes in the living environment
may be obtained using the analysis of historical aerial photography, with comparison to contemporary imagery. is meth-
od provides the ability to determine the state of elements of the space over a long period, encompassing the time when it
was not possible to acquire the data from satellite imagery or some other contemporary sources. Aerial images are suitable
for mapping spatial phenomena with relatively limited spatial distribution because they possess a high level of details and
low spatial coverage. With a comparative analysis of aerial imagery from the past, contemporary aerial imagery, and other
sources of aerial imagery, we can obtain information about the nature and trends of the observed phenomena as well as
directions of future actions, considering changes detected in the environment, whether they are preventive or corrective in
nature. is paper gives the methodological framework for the appliance of the existing knowledge from various elds, in-
tending to use historical aerial photography for monitoring of environmental changes of the Bovan Lake in Eastern Serbia.
Keywords: environment, landscape, historical aerial photography, photography processing, mapping, remote sensing, spa-
tial analysis.
Introduction
e detection of previous and analysis of the current state,
scope, and prediction of changes, is a method commonly
used for exploration of a given area. Usage of aerial im-
agery provides a solid base for numerous researches e.g.
in the landscape and living environment generally (Gong,
2012; Liu & Yang, 2018; Melnyk, 2008). Concerning oth-
er data acquisition methods, including satellite imagery,
the advantages are obvious – e.g. full autonomy in work,
306 S. Bakrač et al. Using historical aerial photography for monitoring of environment changes: a case study of Bovan...
Antrop, 2004). e aerial imagery from previous and
present day’s epoch, together with equipment for pro-
cessing and visualization of geospatial data e.g. give the
multipurpose ability (Collier etal., 2001; Dyce, 2013).
us, by analyzing the aerial imagery, important and
high-quality spatial information can be acquired (with
high accuracy), e.g. its previous and actual state (Kull,
2005; Tucker, 1979). An aerial image gives a great oppor-
tunity for analysis of surface spatial elements such as soil,
vegetation, urban areas, transportation, hydrography, etc.
(Morgan etal., 2010).
e area of interest treated in this paper is a narrow
area around Bovan Lake and the main goal is to get the
answers about changes considering land use, vegetation,
extent, and type of transportation, and the degree of hy-
drographical changes over time. e secondary goal is to
show the way of preparation and possibilities of usage of
historical aerial photography for environmental analysis
and other elds of the research.
is paper covers the period of over 40 years through
the three epochs of the camerawork: 1969, 1980, and 2010.
For the research purposes, aerial imagery in possession of
the Military Geographical Institute [MGI] was used. In
the rst part of this project, it was necessary to digitize
the aerial imagery from the rst and the second epoch.
For that purpose, well-known methods of digitalization
were used (Bakrač etal., 2018; Wrobel, 1991). e further
procedure of processing all aerial images (all 3 epochs)
implied the creation of the orthophoto. Aer the creation
of the orthophoto (standard activities were carried out),
we started the mapping process according to well-known
methods (Redecker, 2008; Sonnentag, 2009; Wolf etal.,
2014). Mapping of the content for every given epoch was
conducted through processes of 3D and 2D photogram-
metrical plotting, with the usage of adequate soware and
standard methods, respective up-to-date approaches, and
tendencies (Conte etal., 2014; Firoz etal., 2018; Li etal.,
2016). For the mapping purposes, basic spatial features
were chosen: hydrography (rivers, lakes), main roads, lo-
cal roads, residential and vegetation, similar to (Butt etal.,
2015; Haque & Basak, 2017; Valta-Hulkkonen etal., 2005).
Possibility of usage of the more thorough data model is
also an option, but it requires additional eldwork, desk-
top research, and redenition of the main goal of the
monitoring (Jackson etal., 2016; Rawat & Kumar, 2015;
Szatmári etal., 2016). For every individual epoch and ac-
cording to every feature mapped, the results are shown
both graphically and numerically (tabular), similar to the
examples (Alphan, 2003; Pugmacher etal., 2012; Salinas-
Melgoza etal., 2018). e analysis of the results of the
treated features was conducted both separately and by
the comparison between each epoch, like in (Cissel etal.,
2011; Daigle, 2010; Liu & Yang, 2018).
We assume that the methodology used and the results
presented would cause, and potentially initiate, more thor-
ough analysis and the monitoring of the Bovan Lake, as
well as other areas.
1.Method and materials
Location description and choice of materials
e Bovan Lake is located in the eastern part of the Re-
public of Serbia, near the Bovan settlement, in the region
called Sokobanjska Moravica. e lake itself is an arti-
cial accumulation, which was formed by the creation of a
dam on the Moravica River. e reason for the building
of such a dam was the regulation of the Moravica River
basin, with the main goal of ood control, prevention of
the accumulation of sludge and dirt from the Mountain
Rivers, and the water supply of the larger settlements that
are located in the vicinity (Aleksinac, Soko Banja, Ražanj).
e Bovan Lake is 14 kilometers away from the Alek-
sinac town and 9 kilometers from the Soko Banja. It is
located near the regional road Aleksinac-Soko Banja-
Knjaževac. e lake is 8 kilometers long with its maxi-
mum width of 500 meters and 50 meters depth. At the
Bovan Lake banks, there are two settlements – Bovan
and Trubarevac, and a number of weekend resorts. e
body of the dam is constructed by laying the stone with
the central core made of clay and it possesses additional
ltration layers. e dam itself is 52 meters high, with 151
meters of length at its top and 6 meters of crown width.
e total gross capacity of accumulation is 58.75 million
m3, with a useful capacity of 19.5 million m3. Since the
creation of accumulation, the analysis of the impact on the
living environment was not performed, except for the part
that considered the quality of the water and the amount
of deposited mud. e water quality is not at the required
level and if this trend continues with the same intensity,
the accumulation lament will reach a normally slowed
level in 93 years. By analyzing the aerial imagery from
the archive of the MGI for the area of the interest, aerial
images that meet the given criteria, which means epoch,
scale, and quality were chosen (MGI). e aerial imagery
from three epochs were chosen and those are aerial im-
ages from 1969, 1980, and 2010. Imagery that originates
from 1969 are panchromatic images taken at 1:32500 scale
using an RC-8 camera with the focus of 153.3. Imagery
from 1980 is also panchromatic, at 1:26000 scale taken by
RC-10 camera with the focus of 115.24. e most up-to-
date imagery from 2010 were taken with an UltraCamXP
digital photogrammetric camera, with a 40 cm resolution.
It is worth mentioning that all of the listed aerial images
were collected for topographic map creation purposes.
2. Data preparation and processing model
For the epochs of acquisition from 1969, and 1980, it was
necessary to scan aerial images. Aerial images taken dur-
ing these two epochs were in the form of analogue aerial
images so they had to be transformed into digital form in
order to conduct the analysis. For those purposes, well-
known digitalization methods were used (Bakrač etal.,
2018; Wrobel, 1991) together with the DeltaScan-6 scan-
ner. e main goal of primary processing is the creation
Journal of Environmental Engineering and Landscape Management, 2021, 29(3): 305–317 307
of orthophoto images for the given epochs of acquisition
(Figures1, 2, and 4). e entire procedure was conducted
through the following activities: provision of the digital
elevation model, provision of the control points, ortho-
rectication of individual lines of the imagery, the radio-
metric correction of digital orthophotos, mosaicking of
the orthorectied images, assembling of the orthophoto
map and nally, quality control of created orthophoto map
(Wolf etal., 2014).
e orthophoto images were produced in the LPS (Er-
dasImagine soware package) using a similar workow
and method as in (Pinto etal., 2019). e results are given
in Figure1.
3. e data modelling in 3D and 2D restitution
e photogrammetrical 3D processing was conducted for
the purpose of the creation of the elevation terrain model
(Wolf etal., 2014) in form of the digital terrain model
(DTM) and the acquisition of the information about the
relative height of the features. 2D processing was per-
formed in order to acquire planimetrical orthorectied
data from the images (Linder, 2009; Pinto etal., 2019).
Based on the stereo models created in the phase of prepa-
ration of these data for the photogrammetrical 3D map-
ping, the extension for ArcGIS – ERDAS Stereo Analyst
for ArcGIS was used.
For the purpose of the 2D processing, from the base
DTM, height points and lines were generated and, the
content created in the 3D restitution was adapted for the
usage in the 2D restitution in the central geodatabase
environment. Data processing is conducted according to
the existing data model, with respect to basic cartographic
and topological rules as well as the symbology for mapped
elements.
Figure 1. e appearance of the „raw“ image (le image) and orthorectied image (right image) from 1980 (source: MGI, 2020)
Figure 2. Orthophoto based on the historical aerial photographs from 1969 (source: MGI, 2020)
308 S. Bakrač et al. Using historical aerial photography for monitoring of environment changes: a case study of Bovan...
3.1. 3D restitution method
e goal of data acquisition for spatial features in 3D is the
information about the terrain height or the feature heights
related to terrain (Linder, 2009). For the purpose of this
research, the DTM, which is generated based on the vec-
tor level lines shown on the topographic maps created by
MGI at 1:25000 scale, was used (MGI) (Figure3).
Figure 3. Perspective view of the Bovan Lake area (ortophoto
from 2010 over the DTM) (source: MGI, 2020)
e level of detail and the accuracy of this model fully
meet the requirements which are necessary for conducting
this analysis. For example, if analysis of the inuences and
the estimation of the potential ood damage is required
(Kourgialas & Karatzas, 2011), this model would be able
to provide good estimation, but, for the purpose of under-
taking measures for ood protection, more accurate and
detailed DTM would be required. e contemporary ten-
dency is, that for the creation of the more accurate DTM,
LIDAR systems are to be used (Firoz etal., 2018; Liu etal.,
2007). Such data, taken from the LIDAR sensors, in the
combination with the aerial photogrammetrical data,
would be able to provide accurate, three-dimensional in-
formation about the spatial features and their behavior,
whether they are natural (land surface, vegetation) or ar-
ticial features.
3.2. 2D restitution method
Aer the phase of the 3D restitution, the 2D restitution
phase (mapping) was performed. e rst step was the
adjustment of the 3D data, which were taken from their
natural 3D environment, for usage in the 2D environment.
e second phase considered mapping and processing of
the rest of the elements according to the adopted logical
data model, physical data model, and the corresponding
symbology in the ESRI soware. e required attributes
for the corresponding elements were also stored in the
geodatabase. In this phase of the 2D restitution, previously
mentioned cartographic sources were used with the respect
to the standard cartographic rules (Robinson & Kimerling,
1995), elements of the hydrography were mapped rst, fol-
lowed by the road structure as well as the other commu-
nications and the features related to them followed by the
anthropological constructions and the vegetation coverage.
In order to group all of the collected data in one single
Figure 4. Orthophoto based on the aerial photographs from 2010 (source: MGI, 2020)
Journal of Environmental Engineering and Landscape Management, 2021, 29(3): 305–317 309
place for easier processing and additional possibilities, the
central geospatial database was created, from which it was
possible to generate special maps and dierent views. e
advantages of the production process based on the central
geospatial database are numerous, from which, maybe the
most important are easier data access, multiscalar view,
easier data administration, redundancy elimination, for-
malization, and standardization (Atzeni etal., 1999). is
approach decreased the time necessary for the creation of
special maps required for this research (Figures5, 6 and 7).
4. Results
e results obtained by the analysis of the historical or-
thophoto images are shown according to the year of the
acquisition (1969, 1980, 2010), graphically as well as ana-
lytically. e graphical presentation is performed through
mapping, by creating special maps respectively (Figures5,
6, and 7). Analytically, the areas and lengths of the treated
entities were analyzed and presented in Figures8 and 9,
Table 5. e mapping was performed directly in a central
geodatabase. By using the basic functions of the database,
we were able to get numerical values for shapes (areas)
and lengths for analyzed features. e data models, both
logical and physical, are structured using the standard ap-
proach (Atzeni etal., 1999). e total areas of the image
blocks, according to the epoch of the time of the acquisi-
tion are equivalent. From the features that were identied
on the acquired images, eight were found to be signicant
for mapping for this research. ese features are hydrog-
raphy (rivers, lakes), the road network (main roads, local
roads), constructions, and vegetation (forests, orchards,
bushes). e same number of analyzed features were used
in similar researches (Butt etal., 2015; Haque & Basak,
2017; Valta-Hulkkonen etal., 2005).
All of the features were mapped, the hydrography was
limited to the Moravica River and the boundaries of the
Bovan Lake. e mapped vegetation coverage was treated
as the forests, bushes/low plants, and the orchards and
the road network, according to their signicance, as main
or the local roads. e areas that were classied as the
meadows, construction sites, and urban areas were not
mapped since they possess very low or no impact at all on
the results of this research. Nevertheless, the option for the
more detailed classication of the basic features remains
open, which would allow more comprehensive analysis
(Fensham & Fairfax, 2002; Jackson etal., 2016; Morgan
& Gergel, 2013). is approach requires a previous analy-
sis of the features that need to be collected and analyzed.
Also, it would be necessary to precisely dene the goals of
such monitoring, as were done in (Sun, 2000a; Szatmári
etal., 2016; Valta-Hulkkonen etal., 2005). It is always pos-
sible to upgrade or change the data models according to
the new requirements with (mostly) insignicant engage-
ment of resources and adaptation of the existing data to
the new data model (which requires (mostly) signicant
engagement of the resources).
4.1. Analysis of constructions monitoring
By analyzing the constructions (Table1, Figure8) it is de-
termined that the number of constructions for the time
period from 1969 to 2010 – between the rst and the last
image acquisition, increased from 889 (in 1969) to 1179
(in 2010) with the middle epoch (1980) showing 537 con-
structions.
Table 1. Constructions in the area around the Bovan Lake
during 1969–1980–2010
1969 1980 Change
%2010 Change
%
Change
%
Number Number 1980–
1969 Number 2010–
1980
1969–
2010
889 537 –40 1179 +119.6 +32
Figure 5. e mapped content from 1969 (source: MGI, 2020)
310 S. Bakrač et al. Using historical aerial photography for monitoring of environment changes: a case study of Bovan...
It is evident that the incrementation in the number
of the constructions is caused by an increased number of
residential constructions, which are mostly located in the
vicinity of the banks of Bovan Lake, which implies that
those are weekend cottages.
4.2. e analysis of the vegetation coverage
monitoring
e analysis of the total changes of the vegetation coverage
that occurred over the analyzed period shows that in 1969,
1278ha was under the vegetation coverage, in 1980, the
vegetation coverage spread over 1325ha of the analyzed
area and, about 1791ha in 2010. A signicant increase in
the vegetation coverage (over 35% increase) had occurred
in the period between 1980 and 2010. e total increase
of the vegetation coverage for the analyzed period is 40%
(Table2, Figure9).
e area under the forest was growing continuously
from 817ha in 1969, over 924 ha in 1980, to 1576ha in
2010. In the summary of the vegetation coverage, forests
took the largest percent. Such as the percentage of the for-
ests in the total vegetation coverage took 63.9% in 1969,
69.8% in 1980, and 88% in 2010. For the same time pe-
riod, the lower vegetation took 352ha (27.6% of the total
area under vegetation coverage) in 1969, 326ha in 1980.
(–7.6%) and reached the level of 130 ha in 2010, which is
a signicant decrease (–63%) in comparison to 1969. e
area under orchards has slightly recovered over the pe-
riod between 1980 and 2010, compared to the time period
between 1969 and 1980 when the percentage of the area
under orchards was decreased by 31.4%.
When summarized, for the time period between 1969
and 2010, a decrease of 21.5% of the area under orchards
is evident. is decrease in the area under orchards is con-
sidered to be the direct consequence of the inundation
and the very creation of Bovan Lake and, indirectly, the
consequence of the decrease in the number of agricultural
households.
4.3. e analysis of the hydrography monitoring
As mentioned, two hydrography features were analyzed,
the Moravica River and the Bovan Lake. Aer the analysis,
it is concluded that the Moravica River was inundated by
the creation of the Bovan Lake accumulation in a length of
9.1 km (Table3, Figure8). In the analyzed area, from the
Moravica River, only 1.7 km of the river ow remained,
which is an 84% decrease in comparison to the length
which is recorded on historical aerial photography from
1969. It is important to note that, according to the histori-
cal aerial photography from 1980, the entire area of inter-
est was partly inundated, thanks to the work in progress
on the Bovan dam. e calculated area of the Bovan Lake,
while in full capacity, based on the aerial photography
from 2010 is 2,191,584.2m2 or about 219ha.
Table 2. e vegetation in the area around the Bovan Lake during 1969–1980–2010
Land use/cover categories
1969 1980 Change % 2010 Change % Change %
Square
meters-m2%Square
met.-m2% 1980–1969 Square met.-m2%2010–
1980
1969–
2010
Total Vegetation 1,278,3226 m2100 1,325,0283 m2100 +3.65 1,790,9614.1 m2100 +35 +40.1
Vege-
tation
forest 817,1342.1 63.9 924,6628.1 69.8 +13.2 1,575,6815.6 88 +70.4 +92.8
bush/low plants 352,8506.2 27.6 326,0669.6 24.6 –7.6 130,2271.2 7.3 –60 –63.1
orchard 108,3377.7 8.5 74,2985.3 5.6 –31.4 85,0527.3 4.7 +14.5 –21.5
Figure 6. e mapped content from 1980 (source: MGI, 2020)
Journal of Environmental Engineering and Landscape Management, 2021, 29(3): 305–317 311
4.4. e analysis of road network monitoring
e analysis of road network was conducted based on the
mapped content, considering changes in the length of the
main roads (solid/asphalt base) and the local (dirt) roads
(Table4, Figure9). e given results show that, in the road
network structure, local roads are dominant over the main
roads and that the total infrastructure length, for the time
period between 1969 and 2010 was cumulatively increased
by 11.8%. For the time period between 1969 and 1980, the
total road network length was decreased by 21.2%. When
summarized, for the time period from 1969 to 2010, it is
noted that the length of the local roads is increased by 2%
compared to the length of the main roads. It is also nota-
ble that the main road length was increased by 83.5% in
the given time period, while, at the same time, the length
of the local road was decreased by 36.1%. e changes in
the road network structure were not linear over the given
time period. From 1980 to 2010, the decrease in the main
road length was almost 40% and the increase in the local
road length was 75.5%.
Table 3. Hydrography in the area around the Bovan Lake during 1969–1980–2010
Land use/cover
categories
1969 1980 Change % 2010 Change % Change %
Square
meters-m2
Length-m
%Square meters-m2
Length-m % 1980–1969
Square
meters-m2
Length-m
% 2010–1980 1969–2010
Hydro-
graphy
Moravica
River-m 10 859.2 m 100 8273.2 m 100 –23.8 1696.6 m 100 –79.5 –84.4
Bovan
Lake-m20 m2054,2829 m2100 +100 219,1584.2 m2100 +303.7 +303.7
Figure 7. e mapped content from 2010 (source: MGI, 2020)
Table 4. Roads in the area around the Bovan Lake during 1969–1980–2010
Land use/cover
categories
1969 1980 Change
%2010 Change
%
Change
%
Length-m % Length-m % 1980–
1969 Length-m % 2010–
1980
1969–
2010
Total Road 85,982.5 m 100 67,752.3 m 100 –21.2 96,147 m 100 +41.9 +11.8
Road
main road-
asphalt 10,732.5 m 12,5 19,696.7 m 29 +83.5 11,824.2 m 12.3 –40 +10.2
local road-
macadam+
dirt road
75,250 m 87,5 48,055.6 m 71 –36.1 84,322.9 m 87.7 +75.5 +12.1
312 S. Bakrač et al. Using historical aerial photography for monitoring of environment changes: a case study of Bovan...
5. Discussion
Our research had presented a successful implementation
of the new work process for the preparation of histori-
cal aerial imagery for monitoring changes in the environ-
ment. e local case study has been developed in Eastern
Serbia, over the narrower territory of Bovan Lake. e
methods applied have yielded the methodological frame-
work, which contains multiple methods and processes.
Procedures and equipment used (aerial acquisition cam-
eras, photogrammetric scanners, photogrammetric and
cartographic soware, etc.) have been described. is
framework had shown that the spatial information ob-
tained may be used with the accuracy required for the de-
scription and understanding of the environmental events
and processes. We believe that our methodological frame-
work has the application for a broad range of researchers
and elsewhere, for the ones interested in landscape en-
vironmental science, socio-environmental patterns and
dynamics of spatial changes, long-term planning in the
environmental science, and monitoring global changes.
5.1. Method and materials
During the rst phase of the implementation of the photo-
grammetric method, selected aerial images from the MGI
archive have been used for three epochs: 1969, 1980, and
2010. During this phase, we have presented the path from
the collection of the raw information – aerial imagery;
over their processing (scanning and ortho-rectication)
to the orthophoto production (Szatmári etal., 2016).
e data obtained had high geometric accuracy (Pinto
etal., 2019) and were suitable for use in further quality
processing during the next phase of the combined applica-
tion of photogrammetric and cartographic methods (3D
and 2D restitution data modeling). is phase has been
performed aiming at the production of digital maps over
the narrower territory of Bovan Lake. We believe that the
application of our model and methods used provides ex-
ceptional conditions for reconstruction and understand-
ing historical patterns and processes, which are the key
to understanding the emergence of present conditions
(Мorgan etal., 2010; Pinto etal., 2019).
e results expected and obtained indicate that our
working method is well planned and successfully imple-
mented. ere are known examples of monitoring chang-
es of the individual elements (Collier etal., 2001; Tobak
etal., 2008) by the other researchers. ey have, though
in the lesser scope and details (individual features of the
environment) similarities to our research. We believe that
our methodological framework is specic for its compre-
hensiveness and the application of the presented methods.
It is also specic for a unied presentation of the scientic
disciplines applied in photogrammetric and cartographic
processing.
5.2. Hydrography and Constructions in 1969–1980–
2010
e captured state from 2010 shows increase in the num-
ber of constructions by 32% compared to 1969. is in-
formation does not represent a signicant increase in the
number of constructions for the given time period, but it
is directly related to the resulting landscape change caused
by the establishment of the Lake accumulation. During
this research, a signicant increase is identied in the
constructions that are located in the vicinity of the Bovan
Lake banks (Figure8). is is related to the purpose of
these objects because the landscape of the Lake has be-
come attractive for recreational and tourist purposes. A
similar case, but with a higher percentage of the construc-
tion, occurred in the example of the changes in the land
use and urbanization of Adana (Alphan, 2003).
It would be useful to evaluate the risk of the pollution
from this potential source (constructions), as it was done
in the following examples (Kanakiya etal., 2014; Zhang
Figure 8. Map of the hydrography and the constructions change over the years (source: MGI, 2020)
Journal of Environmental Engineering and Landscape Management, 2021, 29(3): 305–317 313
etal., 2008; Gaudėšius, 2020). If the number of the inhab-
itants would also be considered and analyzed according to
chosen epochs, together with correlation to the number
of the constructions as well as other analyzed elements
(Table5), this would facilitate the acquisition of additional
data and the statistical indicators.
Furthermore, mapped and analyzed data for the two
observed hydrographical entities (the Moravica River
and the Bovan Lake) shows that, by the building up of
the accumulation, the entire area suered from signicant
changes in the landscape, and, most likely other changes
in the ecosystem (Figure8). at is yet to be determined,
as it was through numerous researches (Arango & Tank,
2008; Johnson & Host, 2010; Wagner et al., 2007). e
analysis of the analytic map (Figure8) shows the land-
scape change – the appearance of the Lake and the disap-
pearance of the Moravica River. e area covered by the
Lake ranged from 0 hectares in 1969 over 54.3 hectares in
1980 to 219.2 hectares in 2010. e loss of the river ow
of the Moravica River in the length of 9.2km is a direct
consequence of the formation of the Bovan Lake as an
articial accumulation. is change in aquatic ecosystems
has probably caused numerous changes in the living world
therein. Also, it is considered that the appearance of the
accumulation aected the increase in the number of the
objects and the present water pollution, which is stated in
the report on the water management strategy on the ter-
ritory of the Republic of Serbia (Government of Republic
of Serbia [GRS], 2015, p.90). is research did not aim
to determine the ecosystem changes and the water quality
of the subject area, but it should be determined by some
other research according to known examples (Battarbee
& Bennion, 2011; Davidson & Jeppesen, 2013; Ko etal.,
2016; Baltrūnas etal., 2020).
Table 5. Elements and size of environmental changes in the area of the Bovan Lake from 1969 to 1980 to 2010
Land use/cover
categories
1969 1980 Change % 2010 Change % Change %
-Number
-Square
meters-m2
-Length-m
%
-Number
-Square
met.-m2
- Length-m
%1980–
1969
-Number
-Square met.-m2
- Length-m
%2010–
1980
1969–
2010
Total Constructions 889 100 537 100 –40 1179 100 +119.6 +32
Total Vegetation 1,278,3226 m2100 1,325,0283m2100 +3.65 1,790,9614.1 m2100 +35 +40.1
Vege-
tation
Forest 817,1342.1 63.9 924,6628.1 69.8 +13.2 1,575,6815.6 88 +70.4 +92.8
Bush 352.8506.2 27.6 326,0669.6 24.6 –7.6 130,2271.2 7.3 –60 –63.1
Orchard 108,3377.7 8.5 74,2985.3 5.6 –31.4 85,0527.3 4.7 +14.5 –21.5
Hydro-
graphy
River-m 10859.2 m 100 8273.2 m 100 –23.8 1696.6 m 100 –79.5 –84.4
Lake-m20 m2054,2829 m2100 +100 219,1584.2 m2100 +303.7 +303.7
Total Road 85,982.5 m 100 67,752.3 m 100 –21.2 96,147 m 100 +41.9 +11.8
Road
main road-
asphalt 10,732.5 m 12.5 19,696.7 m 29 +83.5 11,824.2 m 12.3 –40 +10.2
Local road-
macadam+
dirt road
75,250 m 87.5 48,055.6 m 71 –36.1 84,322.9 m 87.7 +75.5 +12.1
Figure 9. Map of the road network and the vegetation change per years (source: MGI, 2020)
314 S. Bakrač et al. Using historical aerial photography for monitoring of environment changes: a case study of Bovan...
5.3. Road network and Vegetation in 1969–1980–
2010
e analysis of the road network shows that the local
roads are dominant over the main roads and that the to-
tal road length is increased by 11.8%. It is considered that
the main reason for lowering the total length of the main
roads was inundation caused by the building up of the ac-
cumulation. e newest captured state (2010) points to the
need for the determination of the ecological impacts on
the road structure (Figure9). at should be done by di-
rect research, as it was done in (Daigle, 2010; Spellerberg,
1998; Switalski etal., 2004). It would also be very useful to
establish ecological and comprehensive monitoring over
the existing roads in order to track and predict dierent
inuences, as in examples (Sun, 2020b; Drobnjak etal.,
2016; Falk etal., 2007).
About the predictions of the future condition of the
road network and its impact on the given area of interest,
it is very dicult to calculate and give the exact predic-
tion. It is highly likely that the current state of the main
roads will stay unchanged in the close future since that
further urbanization in a given area is not expected. For
the local roads, especially dirt roads, it is not possible
to give any serious prediction, since this category of the
roads is not systematically maintained and is not of sig-
nicant importance.
e evident changes in the vegetation coverage struc-
ture are mostly pointing at a noticeable increase in the area
under the forest over the area under the lower vegetation.
is fact is also pointing out the aux of the overground
vegetation biomass. For the conrmation of the accrual
of the biomass and its exact quantities, it would be neces-
sary to carry out additional procedures, as in Malhi etal.
(2006). e presumption is that the low vegetation grew
to become a forest, mostly because of the lack of active
households, which is directly related to the appearance of
the accumulation. Decrease of the area under the orchards
by 21.5% is also directly related to the accumulation, and
that decrease is due to two reasons. Over the rst period,
the main reason was inundation, which was initiated by
the creation of the accumulation, and, in the second pe-
riod, the lack of active households. e total analysis of
the vegetation coverage for the time period from 1969 to
2010 also gives the prediction that in the future, the trend
of the domination of the high vegetation over the low
vegetation will continue, which is known from the cases
of vegetation monitoring in the examples (Pugmacher
etal., 2012; Salinas-Melgoza etal., 2018). Based on the
obtained data, together with the additional eld check,
the risk of the potential re hazard could be identied as
well, as was done in (Bańales-Seguel etal., 2018). ese
data point out the fact that the cultivation in this area is
signicantly lowered. Based on the measured values, there
is a possibility of future analysis and the interpretation of
the state and the structure of the vegetation coverage using
both statistical and probability methods.
Conclusions
is paper shows the application of the given methodo-
logical framework for monitoring environmental changes
based on the use of historical aerial photographs. e
given methodological framework was established on the
basis of the integrated appliance of the previously con-
ducted researches from the various elds, such as remote
sensing, GIS and cartography. e results of the applied
methodology show the possibilities and potential of us-
age of the historical aerial photographs for the purpose of
the monitoring of the spatial changes in the surroundings
of the articial accumulation – the Bovan Lake. e data
that was used for the analysis dates from three dierent
epochs and covers the time period of over 40 years. In this
manner, the relevant and accurate information is gathered
considering the dierent ways and the quantities that the
creation of the articial accumulation – the Bovan Lake,
aected the chosen elements of the given area.
By this research, the following elements of space were
analyzed: construction, main roads, local roads, rivers,
lakes, orchards, forests, low plants (Figures8 and 9; Ta-
bles1, 2, 3, 4 and 5). Each of the analyzed spatial features
is dominant in the observed ecosystem and, as such, can
be presented as a feature, entirety, corridor, or matrix. e
acquired data gives the opportunity to analyze relatively
small features as well (bush, individual residential con-
struction) and the advanced visual overview of the en-
vironment.
It is considered that by the construction of the articial
accumulation, the total ecosystem stability of the environ-
ment was lowered, the new spatial element appeared, but
the diversity and the homogeneity (the larger area under
forest and the water) are experiencing signicant expan-
sion. is analysis shows that the anthropogenic inuence,
considering most of the spatial features, is relatively insig-
nicant and that the changes that occurred are the result
of the spontaneous changes in the environment. e only
signicant change is in the hydrography layer (river, lake)
as a consequence of dam construction and, more directly,
the creation of the articial accumulation. Regarding the
observed landscape, there is a growing body of the ques-
tions, such as: how does it function, is there any com-
munication exchange among the biotic, abiotic factors,
energy, and information, and how extensive is it, which
did not get the answers during this research. For these
questions to be answered, additional research would be
required.
It is ascertained that over the time period of over 40
years, a new landscape progressively replaced the previ-
ous one, which could be seen from the usage of historical
aerial photography. In the following period, it would be
useful to continue monitoring the changes of all the ob-
served features of the given landscape.
is research gives a model for the preparation and
processing of aerial imagery as well as its usage in the cen-
tral database environment. e preparatory actions of the
Journal of Environmental Engineering and Landscape Management, 2021, 29(3): 305–317 315
aerial photography and the orthorectication process had
fullled the prerequisites for their further processing. In
this manner, the spatial models were created according to
the situation at the moment of the aerial imagery acqui-
sition. By the implementation of these procedures, every
aerial image was transformed from the central to the or-
thogonal projection. e primary processing of the aerial
imagery was conducted through the methods of the pho-
togrammetrical 3D and 2D data processing. Further data
processing was conducted through vectorisation method
in the central database environment while respecting the
basic cartographic and the topographic rules as well as the
symbology of the mapped elements. e data collected in
that manner was used to perform the mapping process of
the features in the area of interest.
It can be concluded that this paper gives the demon-
stration of the possibilities of the usage and the quality of
the historical aerial photography for the spatial analysis
and the other research that include the living environ-
ment in general. is approach should provide a better
understanding and the monitoring of the spatial changes
that occur over time as well as the possibility to recog-
nize the changing patterns. e analysis showed that this
model, with high scientic reliability, may be used for the
research.
References
Alphan, H. (2003). Land‐use change and urbanization of Adana,
Turkey. Land Degradation & Development, 14(6), 575–586.
https://doi.org/10.1002/ldr.581
Arango, C. P., & Tank, J. L. (2008). Land use inuences the spa-
tiotemporal controls on nitrication and denitrication in
headwater streams. Journal of the North American Benthologi-
cal Society, 27(1), 90–107. https://doi.org/10.1899/07-024.1
Atzeni, P., Ceri, S., Paraboschi, S., & Torlone, R. (1999). Basi di
dati, seconda edizione. McGraw-Hill.
Bakrač, S., Drobnjak, S., Stanković, S., Vučićević, A., &
Stamenković, N. (2018). Preparation of photogrammetric ar-
chive documentation for scientic and other research. Sinteza,
2018, 17–22. https://doi.org/10.15308/sinteza-2018-17-22
Baltrūnas, V., Slavinskienė, G., Karmaza, B., & Pukelytė, V.
(2020). Eectiveness of a modern landll liner system in
controlling groundwater quality of an open hydrogeological
system, SE Lithuania. Journal of Environmental Engineering
and Landscape Management, 28(4), 174–182.
https://doi.org/10.3846/jeelm.2020.13730
Bańales-Seguel, C., De la Barrera, F., & Salazar, A. (2018). An
analysis of wildre risk and historical occurrence for a medi-
terranean biosphere reserve, central Chile. Journal of Envi-
ronmental Engineering and Landscape Management, 26(2),
128–140. https://doi.org/10.3846/16486897.2017.1374280
Battarbee, R. W., & Bennion, H. (2011). Palaeolimnology and its
developing role in assessing the history and extent of human
impact on lake ecosystems. Journal of Paleolimnology, 45(4),
399–404. https://doi.org/10.1007/s10933-010-9423-7
Butt, A., Shabbir, R., Ahmad, S. S., & Aziz, N. (2015). Land use
change mapping and analysis using Remote Sensing and GIS:
A case study of Simly watershed, Islamabad, Pakistan. e
Egyptian Journal of Remote Sensing and Space Science, 18(2),
251–259. https://doi.org/10.1016/j.ejrs.2015.07.003
Cissel, R., Fly, C., Black, T., Luce, C., & Staab, B. (2011). Legacy
roads and trails monitoring project. Road decommissioning
in the Bull Run River watershed, Mt. Hood National Forest.
https://www.fs.fed.us/GRAIP/downloads/case_studies/Lega-
cyRoadsMtHoodNF_BullRunRiver2008Decomission_Final-
Report0713.pdf
Collier, P., Inkpen, R., & Fontana, D. (2001). e use of historical
photography in environmental studies. Cybergeo: European
Journal of Geography. https://doi.org/10.4000/cybergeo.4019
Conte, G., Rudol, P., & Doherty, P. (2014). Evaluation of a light-
weight LiDAR and a photogrammetric system for unmanned
airborne mapping applications. Photogrammetrie-Fernerkund-
ung-Geoinformation, 2014(4), 287–298.
https://doi.org/10.1127/1432-8364/2014/0223
Daigle, P. (2010). A summary of the environmental impacts of
roads, management responses, and research gaps: A literature
review. Journal of Ecosystems and Management, 10(3), 65–89.
http://jem-online.org/forrex/index.php/jem/article/view/38/9
Davidson, T. A., & Jeppesen, E. (2013). e role of palaeolim-
nology in assessing eutrophication and its impact on lakes.
Journal of Paleolimnology, 49(3), 391–410.
https://doi.org/10.1007/s10933-012-9651-0
Drobnjak, S., Sekulović, D., Amović, M., Gigović, L., & Rego-
dić,M. (2016). Central geospatial database analysis of the
quality of road infrastructure data. Geodetski Vestnik, 60(2),
270–284.
https://doi.org/10.15292/geodetski-vestnik.2016.02.269-284
Dyce, M. (2013). Canada between the photograph and the map:
Aerial photography, geographical vision and the state. Journal
of Historical Geography, 39, 69–84.
https://doi.org/10.1016/j.jhg.2012.07.002
Falk, D. A., Miller, C., McKenzie, D., & Black, A. E. (2007).
Cross-scale analysis of re regimes. Ecosystems, 10(5), 809–
823. https://doi.org/10.1007/s10021-007-9070-7
Fensham, R. J., & Fairfax, R. J. (2002). Aerial photography for
assessing vegetation change: a review of applications and the
relevance of ndings for Australian vegetation history. Aus-
tralian Journal of Botany, 50(4), 415–429.
https://doi.org/10.1071/BT01032
Firoz, A., Uddin, M. M., & Goparaju, L. (2018). 3D Mapping by
photogrammetry and LiDAR in forest studies. World Scien-
tic News, 95, 224–234. http://www.worldscienticnews.com/
wp-content/uploads/2018/02/WSN-95-2018-224-234-1.pdf
Gaudėšius, R. (2020). Index of anthropogenic load on land
(ALOL) as decision support method in territorial planning.
Journal of Environmental Engineering and Landscape Manage-
ment, 28(3), 116–124.
https://doi.org/10.3846/jeelm.2020.12669
Government of Republic of Serbia. Ministry of Agriculture and
Environmental Protection. (2015). Стратегијa управљања
водама на територији републике Србије. Анализе и
истраживања [Water management strategy on territories of
the Republic of Serbia. Analysis and research]. Jaroslav Cerni
Institute of Water Management. Belgrade. http://www.rdvode.
gov.rs/doc/Strategija%20upravljanja%20vodama.pdf
Gong, P. (2012). Remote sensing of environmental change over
China: A review. Chinese Science Bulletin, 57(22), 2793–2801.
https://doi.org/10.1007/s11434-012-5268-y
Haque, M. I., & Basak, R. (2017). Land cover change detection
using GIS and remote sensing techniques: A spatio-temporal
study on Tanguar Haor, Sunamganj, Bangladesh. e Egyptian
Journal of Remote Sensing and Space Science, 20(2), 251–263.
https://doi.org/10.1016/j.ejrs.2016.12.003
316 S. Bakrač et al. Using historical aerial photography for monitoring of environment changes: a case study of Bovan...
Jackson, M. M., Topp, E., Gergel, S. E., Martin, K., Pirotti, F.,
& Sitzia, T. (2016). Expansion of subalpine woody vegeta-
tion over 40 years on Vancouver Island, British Columbia,
Canada. Canadian Journal of Forest Research, 46(3), 437–443.
https://doi.org/10.1139/cjfr-2015-0186
Johnson, L. B., & Host, G. E. (2010). Recent developments in
landscape approaches for the study of aquatic ecosystems.
Journal of the North American Benthological Society, 29(1),
41–66. https://doi.org/10.1899/09-030.1
Kanakiya, R. S., Singh, S. K., & Sharma, J. N. (2014). Determin-
ing the water quality index of an urban water body Dal Lake,
Kashmir, India. IOSR Journal of Environmental Science, Toxi-
cology and Food Technology, 8(12), 64–71.
https://doi.org/10.9790/2402-081236471
Ko, T., Vandel, E., Marzecová, A., Avi, E., & Mikomägi, A.
(2016). Assessment of the eect of anthropogenic pollution
on the ecology of small shallow lakes using the palaeolim-
nological approach. Estonian Journal of Earth Sciences, 65(4),
221–233. https://doi.org/10.3176/earth.2016.19
Kourgialas, N. N., & Karatzas, G. P. (2011). Flood management
and a GIS modelling method to assess ood-hazard areas – a
case study. Hydrological Sciences Journal – Journal Des Sci-
ences Hydrologiques, 56(2), 212–225.
https://doi.org/10.1080/02626667.2011.555836
Kull, C. A. (2005). Historical landscape repeat photography as
a tool for land use change research. Norsk Geogrask Tidss-
kri– Norwegian Journal of Geography, 59(4), 253–268.
https://doi.org/10.1080/00291950500375443
Li, X., Ming, X., Song, W., Qiu, S., Qu, Y., & Liu, Z. (2016). A
fuzzy technique for order preference by similarity to an ideal
solution-based quality function deployment for prioritizing
technical attributes of new products. Proceedings of the Insti-
tution of Mechanical Engineers, Part B: Journal of Engineering
Manufacture, 230(12), 2249–2263.
https://doi.org/10.1177/0954405416673111
Lillesand, T. M., & Kiefer, R. W. (Eds.). (2000). Remote sensing
and image interpretation (4 ed.). Wiley.
Linder, W. (2009). Digital photogrammetry. Springer.
https://doi.org/10.1007/978-3-540-92725-9
Liu, X., Zhang, Z., Peterson, J., & Chandra, S. (2007). LiDAR-
derived high quality ground control information and DEM
for image orthorectication. GeoInformatica, 11(1), 37–53.
https://doi.org/10.1007/s10707-006-0005-9
Liu, Z., & Yang, H. (2018). e impacts of spatiotemporal land-
scape changes on Water quality in Shenzhen, China. Interna-
tional Journal of Environmental Research and Public Health,
15(5), 1038. https://doi.org/10.3390/ijerph15051038
Malhi, Y., Wood, D., Baker, T. R., Wright, J., Phillips, O. L.,
Cochrane, T., Meir, P., Chave, J., Almeida, S., Arroyo, L.,
Higuchi, N., Killeen, T. J., Laurance, S. G., Laurance, W. F.,
Lewis,S.L., Monteagudo, A., Neill, D. A., Núñez Vargas,P.,
Pitman, N. C. A., Alberto Quesada, C., Salomão,R., Sil-
va,J.N.M., Torres Lezama, A., Terborgh, J., Vásquez Mar-
tínez, R., & Vinceti, B. (2006). e regional variation of
aboveground live biomass in old‐growth Amazonian forests.
Global Change Biology, 12(7), 1107–1138.
https://doi.org/10.1111/j.1365-2486.2006.01120.x
Melnyk, A. (2008). Ecological analysis of landscapes. Methodol-
ogy of Landscape Research. Commission of Cultural Land-
scape of Polish Geographical Society. Sosnowiec.
http://krajobraz.kulturowy.us.edu.pl/publikacje.artykuly/me-
todologia/melnyk.pdf
Military Geographical Institute. (2020). Archive photo documen-
tation. Photogrammetry Department, Cartography Depart-
ment, GIS Department.
Morgan, J. L., & Gergel, S. E. (2013). Automated analysis of aerial
photographs and potential for historic forest mapping. Cana-
dian Journal of Forest Research, 43(8), 699–710.
https://doi.org/10.1139/cjfr-2012-0492
Morgan, J. L., Gergel, S. E., & Coops, N. C. (2010). Aerial pho-
tography: A rapidly evolving tool for ecological management.
BioScience, 60(1), 47–59.
https://doi.org/10.1525/bio.2010.60.1.9
Newton, A. C., Hill, R. A., Echeverría, C., Golicher, D., Rey
Benayas, J. M., Cayuela, L., & Hinsley, S. A. (2009). Remote
sensing and the future of landscape ecology. Progress in Physi-
cal Geography, 33(4), 528–546.
https://doi.org/10.1177/0309133309346882
Pugmacher, D., Cohen, W. B., & Kennedy, R. E. (2012). Using
Landsat-derived disturbance history (1972–2010) to predict
current forest structure. Remote Sensing of Environment, 122,
146–165. https://doi.org/10.1016/j.rse.2011.09.025
Pinto, A. T., Gonçalves, J. A., Beja, P., & Honrado, J. P. (2019).
From archived historical aerial imagery to informative or-
thophotos: A framework for retrieving the past in long-term
socioecological research. Remote Sensing, 11(11), 1388.
https://doi.org/10.3390/rs11111388
Rawat, J. S., & Kumar, M. (2015). Monitoring land use/cover
change using remote sensing and GIS techniques: A case
study of Hawalbagh block, district Almora, Uttarakhand, In-
dia. e Egyptian Journal of Remote Sensing and Space Science,
18(1), 77–84. https://doi.org/10.1016/j.ejrs.2015.02.002
Redecker, A. P. (2008). Historical aerial photographs and digital
photogrammetry for impact analyses on derelict land sites
in human settlement areas. e International Archives of the
Photogrammetry, Remote Sensing and Spatial Information Sci-
ences, 37(B8), 5–10. http://citeseerx.ist.psu.edu/viewdoc/dow
nload?doi=10.1.1.436.1357&rep=rep1&type=pdf
Robinson, A. H., & Kimerling, A. (1995). Elements of cartogra-
phy. Wiley.
Salinas‐Melgoza, M. A., Skutsch, M., & Lovett, J. C. (2018). Pre-
dicting aboveground forest biomass with topographic vari-
ables in human‐impacted tropical dry forest landscapes. Eco-
sphere, 9(1), e02063. https://doi.org/10.1002/ecs2.2063
Sonnentag, O. (2009). Neteler, M., Mitasova, H., 2008. Open
Source GIS A GRASS GIS Approach, 3rd ed. Springer, NY,
USA, ISBN 978-0-387-35767-6, 406 pp., USD 99.00, CDN
128.95, EUR 81.95, Hardbound. Computers and Geosciences,
35(11), 2282. https://doi.org/10.1016/j.cageo.2009.08.001
Spellerberg, I. A. N. (1998). Ecological eects of roads and traf-
c: A literature review. Global Ecology & Biogeography Letters,
7(5), 317–333.
https://doi.org/10.1046/j.1466-822x.1998.00308.x
Sun, J. (2000a). Dynamic monitoring and yield estimation of
crops by mainly using the remote sensing technique in Chi-
na. Photogrammetric Engineering and Remote Sensing, 66(5),
645–650. https://pdfs.semanticscholar.org/10ce/bf9bd1b737c-
48869c7e90abda904d43cc7e7.pdf
Sun, L. (2020b). Pollution assessment and source approximation
of trace elements in the farmland soil near the tracway.
Journal of Environmental Engineering and Landscape Manage-
ment, 28(1), 20–27. https://doi.org/10.3846/jeelm.2020.11745
Switalski, T. A., Bissonette, J. A., DeLuca, T. H., Luce, C. H., &
Madej, M. A. (2004). Benets and impacts of road removal.
Frontiers in Ecology and the Environment, 2(1), 21–28.
https://doi.org/10.1890/1540-9295(2004)002[0021:BAIORR]
2.0.CO;2
Szatmári, J., Tobak, Z., & Novák, Z. (2016). Environmental
monitoring supported by aerial photography – a case study
Journal of Environmental Engineering and Landscape Management, 2021, 29(3): 305–317 317
of the burnt down Bugac Juniper Forest, Hungary. Journal of
Environmental Geography, 9(1–2), 31–38.
https://doi.org/10.1515/jengeo-2016-0005
Tobak, Z., Szatmári, J., & van Leeuwen, B. (2008). Small for-
mat aerial photography – remote sensing data acquisition for
environmental analysis. Journal of Environmental Geography,
1(3–4), 21–26. http://www.geo.u-szeged.hu/journal/sites/de-
fault/les/article_le/4Tobak-et-al-2008-3-4.pdf
Tucker, C. J. (1979). Red and photographic infrared linear com-
binations for monitoring vegetation. Remote Sensing of Envi-
ronment, 8(2), 127–150.
https://doi.org/10.1016/0034-4257(79)90013-0
Valta-Hulkkonen, K., Kanninen, A., Ilvonen, R., & Leka, J.
(2005). Assessment of aerial photography as a method for
monitoring aquatic vegetation in lakes of varying trophic sta-
tus. Boreal Environment Research, 10(1), 57–66. http://www.
borenv.net/BER/archive/pdfs/ber10/ber10-057.pdf
Van Eetvelde, V., & Antrop, M. (2004). Analyzing structural and
functional changes of traditional landscapes – two examples
from Southern France. Landscape and Urban Planning, 67(1–
4), 79–95. https://doi.org/10.1016/S0169-2046(03)00030-6
Wagner, T., Bremigan, M. T., Cheruvelil, K. S., Soranno, P. A.,
Nate, N. A., & Breck, J. E. (2007). A multilevel modeling ap-
proach to assessing regional and local landscape features for
lake classication and assessment of sh growth rates. En-
vironmental Monitoring and Assessment, 130(1–3), 437–454.
https://doi.org/10.1007/s10661-006-9434-z
Wolf, P., Dewitt, B., & Wilkinson, B. (2014). Elements of photo-
grammetry with applications in GIS (4th ed.). McGraw-Hill
Education. https://www.amazon.com/Elements-Photogram-
metry-Application-GIS-Fourth/dp/0071761128
Wrobel, B. P. (1991). e evolution of digital photogrammetry
from analytical photogrammetry. e Photogrammetric Re-
cord, 13(77), 765–776.
https://doi.org/10.1111/j.1477-9730.1991.tb00738.x
Zhang, L., Xia, M., Zhang, L., Wang, C., & Lu, J. (2008). Eutroph-
ication status and control strategy of Taihu Lake. Frontiers of
Environmental Science & Engineering in China, 2(3), 280–290.
https://doi.org/10.1007/s11783-008-0062-4